|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||An online generalized eigenvalue version of Laplacian Eigenmaps for visual big data|
Generalized eigenvalue problem
|Citation:||Malik Z, Hussain A & Wu J (2016) An online generalized eigenvalue version of Laplacian Eigenmaps for visual big data, Neurocomputing, 173 (Part 2), pp. 127-136.|
|Abstract:||This paper presents a generalized incremental Laplacian Eigenmaps (GENILE), a novel online version of the Laplacian Eigenmaps, one of the most popular manifold-based dimensionality reduction techniques which solves the generalized eigenvalue problem. We evaluate the comparative performance of the manifold-based learning techniques using both artificial and real data. Specifically, two popular artificial datasets: swiss roll and s-curve datasets, are used, in addition to real MNIST digits, bank-note and heart disease datasets for testing and evaluating our novel method benchmarked against a number of standard batch-based and other manifold-based learning techniques. Preliminary experimental results demonstrate consistent improvements in the classification accuracy of the proposed method in comparison with other techniques.|
|Rights:||This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Accepted refereed manuscript of: Malik Z, Hussain A & Wu J (2016) An online generalized eigenvalue version of Laplacian Eigenmaps for visual big data, Neurocomputing, 173 (Part 2), pp. 127-136. DOI: 10.1016/j.neucom.2014.12.119 © 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/|
|elsevier-article-accepted-2015.pdf||702.48 kB||Adobe PDF||View/Open|
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